Intelligent Multimedia Technologies for Financial Risk Management Trends, tools and applications
Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images, audio, and full-motion video which can be successfully implemented in banking, financial services and insurance (BFSI) industries to support t...
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| Main Authors | , , , , , |
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| Format | eBook |
| Language | English |
| Published |
Stevenage
The Institution of Engineering and Technology
2023
Institution of Engineering and Technology (The IET) Institution of Engineering & Technology Institution of Engineering and Technology |
| Edition | 1 |
| Series | IET computing series |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9781839536618 1839536616 |
| DOI | 10.1049/PBPC060E |
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| Abstract | Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images, audio, and full-motion video which can be successfully implemented in banking, financial services and insurance (BFSI) industries to support their activities and strategic goals.
This volume provides an overview of multimedia technologies in finance and banking, introduces suitable machine learning and deep learning techniques for financial data analysis, discusses fraud and cyber operation countermeasures for multimedia in financial services, presents concrete applications of natural language processing (NPR) for financial data, introduces robotic process automation technology from the financial market to technology implementation, explains how self-supervised, unsupervised and semi-supervised learning are driving the financial market revolution, and unlocks real-world case studies in multimedia banking across the globe.
The book is intended for professionals involved in multimedia systems and technology design and applications. It can also be used as an advanced text for courses on multimedia. |
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| AbstractList | Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images, audio, and full-motion video which can be successfully implemented in banking, financial services and insurance (BFSI) industries to support their activities and strategic goals.This volume provides an overview of multimedia technologies in finance and banking, introduces suitable machine learning and deep learning techniques for financial data analysis, discusses fraud and cyber operation countermeasures for multimedia in financial services, presents concrete applications of natural language processing (NPR) for financial data, introduces robotic process automation technology from the financial market to technology implementation, explains how self-supervised, unsupervised and semi-supervised learning are driving the financial market revolution, and unlocks real-world case studies in multimedia banking across the globe.The book is intended for professionals involved in multimedia systems and technology design and applications. It can also be used as an advanced text for courses on multimedia. Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images, audio, and full-motion video which can be successfully implemented in banking, financial services and insurance (BFSI) industries to support their activities and strategic goals. This volume provides an overview of multimedia technologies in finance and banking, introduces suitable machine learning and deep learning techniques for financial data analysis, discusses fraud and cyber operation countermeasures for multimedia in financial services, presents concrete applications of natural language processing (NPR) for financial data, introduces robotic process automation technology from the financial market to technology implementation, explains how self-supervised, unsupervised and semi-supervised learning are driving the financial market revolution, and unlocks real-world case studies in multimedia banking across the globe. The book is intended for professionals involved in multimedia systems and technology design and applications. It can also be used as an advanced text for courses on multimedia. This book provides an overview of multimedia technologies used in finance and banking, including ML and DL techniques for financial data analysis, fraud and cyber operation countermeasures, concrete applications of NPR for financial data, self-supervised, unsupervised & semi-supervised learning methods and real-world case studies. Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images, audio, and full-motion video which can be successfully implemented in banking, financial services and insurance (BFSI) industries to support their activities and strategic goals. |
| Author | Balusamy Balamurugan Sood Kiran Gan Gerald Goh Guan Özen Ercan Rawal Bharat Grima Simon |
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| Editor | Grima, Simon Goh Guan Gan, Gerald Rawal, Bharat Sood, Kiran Balusamy, Balamurugan Özen, Ercan |
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| Snippet | Multimedia technologies have opened up a wide range of applications by combining a variety of information sources such as voice, graphics, animation, images,... This book provides an overview of multimedia technologies used in finance and banking, including ML and DL techniques for financial data analysis, fraud and... |
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| SubjectTerms | Artificial intelligence Computer Science COMPUTERS Data analysis: general Financial risk management General Engineering & Project Administration General References Intelligent control systems Multimedia systems Risk assessment Security Software Engineering |
| Subtitle | Trends, tools and applications |
| TableOfContents | Chapter 1: Applications of multimedia in diverse fields: an overview -- Chapter 2: Evolution of multimedia banking and technology acceptance theories -- Chapter 3: Banking, Fintech, BigTech: emerging challenges for multimedia adoption -- Chapter 4: Multimedia technologies in the financial market -- Chapter 5: Data analytics in finance -- Chapter 6: Machine learning and deep learning for financial data analysis -- Chapter 7: Self-supervised, unsupervised & semi-supervised learning for multimedia banking and financial services -- Chapter 8: Natural language processing and multimedia applications in finance -- Chapter 9: Digital disruption and multimedia technological innovations in the banking world -- Chapter 10: Fraud and cyber operation countermeasures for multimedia in financial services -- Chapter 11: Blockchain technology for the financial markets -- Chapter 12: Automation to handle customer complaints: a grievance handling system -- Chapter 13: Robotic process automation applications area in the financial sector -- Chapter 14: Multimedia sustained benefits for financial services -- Chapter 15: Extensive use of multimedia technologies: real-world case studies of multimedia banking -- Chapter 16: Concluding remarks - fintech and technology of today and tomorrow Title Page Call for Authors - The IET International Book Series on Multimedia Information Processing and Security Table of Contents 1. Applications of Multimedia in Diverse Fields: An Overview 2. Evolution of Multimedia Banking and Technology Acceptance Theories 3. Banking, Fintech, BigTech: Emerging Challenges for Multimedia Adoption 4. Multimedia Technologies in the Financial Market 5. Data Analytics in Finance 6. Machine Learning and Deep Learning for Financial Data Analysis 7. Self-Supervised, Unsupervised & Semi-Supervised Learning for Multimedia Banking and Financial Services 8. Natural Language Processing and Multimedia Applications in Finance 9. Digital Disruption and Multimedia Technological Innovations in the Banking World 10. Fraud and Cyber Operation Countermeasures for Multimedia in Financial Services 11. Blockchain Technology for the Financial Markets 12. Automation to Handle Customer Complaints: A Grievance Handling System 13. Robotic Process Automation Applications Area in the Financial Sector 14. Multimedia Sustained Benefits for Financial Services 15. Extensive Use of Multimedia Technologies: Real-World Case Studies of Multimedia Banking 16. Concluding Remarks - Fintech and Technology of Today and Tomorrow Index 9.4 Acceptance for multimedia banking 5.8.10 Simulation -- 5.8.11 Decision tree analysis -- 5.8.12 Sampling technique -- 5.8.13 Standard deviation -- 5.8.14 SAP R/3 vs. ERP -- 5.8.15 Modules for SAP -- 5.9 Conclusion -- References -- 6 Machine learning and deep learning for financial data analysis -- 6.1 Machine learning and deep learning for financial data analysis -- 6.2 Graph neural networks for investor networks analysis -- 6.3 Using ML to predict the defaults of credit card clients -- 6.4 Application of deep learning methods for econometrics -- 6.5 AI and multimedia application in finance -- 6.5.1 AI & -- ML techniques for simulation of markets, economics, and other financial systems -- 6.5.2 Infrastructure to support AI & -- ML research in finance -- 6.5.3 Chatbots & -- robot advisors for payment and innovation -- 6.5.4 AI/ML-based evaluating models -- 6.5.5 Validation and calibration of multi-agent systems in finance -- 6.6 Advance ML for financial stability -- 6.6.1 AI-based blockchain in financial networks -- 6.6.2 Business challenge: deep learning seen as too resource-intensive -- 6.7 Credit scoring models using ML algorithms -- 6.8 Python to implement methods from stochastic -- 6.9 Conclusion -- References -- 7 Self-supervised, unsupervised & -- semi-supervised learning for multimedia banking and financial services -- 7.1 Supervised learning for money-laundering prevention, document analysis and underwriting loans, trade settlements, high-frequency trading -- 7.1.1 What exactly do detecting fraud paradigms perform? -- 7.1.2 Customer experience and segmentation -- 7.1.3 Underwriting and credit scoring -- 7.1.4 Difficulties to industry adoption -- 7.2 Robo-advisors is a tool of supervised learning for optimizing portfolios -- 7.2.1 What is a Robo-advisor? -- 7.2.2 Understanding Robo-advisors -- 7.2.3 Portfolio rebalancing 7.3 Fundamental advantages of Robo-advisors -- 7.4 Hiring a Robo-advisor -- 7.5 Robo-advisors and regulation -- 7.5.1 How Robo-advisors make money -- 7.5.2 The best-in-class Robo-advisors -- 7.6 Component of ML -- 7.6.1 What is PCA? -- 7.6.2 Calculation of PCA -- 7.6.3 Benefits and limitations of PCA -- 7.6.4 Assumptions of PCA -- 7.6.5 Practical working in PCA -- 7.6.6 Production programming for cutting-edge information science -- 7.7 Financial asset clustering using cluster analysis -- 7.8 Latent variable modeling for financial volatility -- 7.9 Association rule learning for financial revenue analysis -- 7.10 Semi-regulated text mining for environmental, social, and governance -- 7.11 Performance of companies -- 7.12 Conclusion -- References -- 8 Natural language processing and multimedia applications in finance -- 8.1 Financial technology and natural language processing -- 8.1.1 NLP-based finance -- 8.2 NLP-based investment management -- 8.2.1 Instances of some key NLP applications in asset management -- 8.3 NLP-based know your customer approach -- 8.4 Applications or systems for FinTech with NLP methods -- 8.5 Crowdfunding analysis with text data -- 8.6 Text-oriented customer preference analysis -- 8.7 Insurance application with textual information -- 8.8 Telematics: motor & -- health insurance -- 8.8.1 Telematics and automobile insurance -- 8.8.2 Benefits of telematics-based auto insurance -- 8.9 Text-based market provisioning -- 8.10 Conclusion -- References -- 9 Digital disruption and multimedia technological innovations in the banking world -- 9.1 Background of multimedia banking -- 9.2 Phases of multimedia banking -- 9.3 Challenges and acceptance of multimedia banking -- 9.3.1 Safety and security -- 9.3.2 System -- 9.3.3 Significant charges -- 9.3.4 Internet connection -- 9.3.5 Customer awareness -- 9.3.6 Cash-dominated rural society 5.2.1 How could back groups conquer the difficulties of working with enormous amounts of information? -- 5.2.2 How does robotization help enormous information examination? -- 5.2.3 How can arising advances enable huge information? -- 5.2.4 How can huge information change finance? -- 5.2.5 What's next for huge information in finance? -- 5.2.6 About cash analytics -- 5.3 Financial time series analysis -- 5.4 Web analytics, visual analytics, service analytics, multimedia analytics, textual data analytics -- 5.4.1 Interactive media analysis -- 5.4.2 Visual analytics -- 5.4.3 Multimedia analysis -- 5.4.4 Interactive media analysis -- 5.4.5 Visual analytics -- 5.5 Predictive, prescriptive, descriptive analytics -- 5.5.1 What is descriptive analytics? -- 5.5.2 What does the spellbinding investigation show? -- 5.5.3 Instances of expressive examination -- 5.5.4 What is diagnostic analytics? -- 5.5.5 What does symptomatic examination show? -- 5.5.6 Instances of demonstrative examination -- 5.5.7 What is predictive analytics? -- 5.5.8 What does the prescient investigation show? -- 5.5.9 Instances of prescient investigation -- 5.5.10 What is prescriptive analytics? -- 5.5.11 What does the prescriptive investigation show? -- 5.6 Expert methods for financial regression and classification problems -- 5.7 Factor models for big data in options stochastic modelling and pricing -- 5.7.1 Bachelier design -- 5.7.2 Scholes-Merton (BS) model in the dark -- 5.7.3 Demand model -- 5.8 Financial mathematical and statistical tools -- 5.8.1 Insertion and extrapolation -- 5.8.2 Decision theory -- 5.8.3 Decision-making under states of assurance -- 5.8.4 Decision-making under states of vulnerability -- 5.8.5 Correlation analysis -- 5.8.6 Cost volume benefit (CVP) or break-even investigation -- 5.8.7 Tests in ventures -- 5.8.8 Serial relationship tests -- 5.8.9 Run tests Intro -- Title -- Copyright -- Contents -- Call for Authors - The IET International Book Series on Multimedia Information Processing and Security -- About the editors -- Foreword - Prof. Ramona Rupeika-Apoga -- Foreword - Series editors Singh and Berretti -- 1 Applications of multimedia in diverse fields: an overview -- 1.1 Introduction -- 1.1.1 Trends in intelligent multimedia data analytics -- 1.2 Tools used in IMDA -- 1.3 Application software used in IMDA -- 1.4 Write an essay on using IMDA in risk management and internal controls -- 1.5 The metaverse -- 1.6 Medical devices -- 1.7 Entertainment -- 1.8 Security -- 1.9 Health -- 1.10 Financial services -- 1.11 Insurance -- 1.12 People's needs and retail shops -- 1.13 Banking services -- 1.13.1 Benefits of e-banking -- 1.13.2 Electronic banking protocols -- 1.13.3 Services -- 1.14 Machine learning -- 1.15 Deep learning -- 1.16 Natural language processing -- 1.17 Blockchain technology -- 1.18 Robotic automatic process -- 1.19 Distributed computing technology -- 1.20 Administrative consistence intricacies -- 1.21 Future technology in finance -- 1.22 Conclusion -- References -- 2 Evolution of multimedia banking and technology acceptance theories -- 2.1 Introduction -- 2.2 Evolution of multimedia in the banking sector -- 2.3 ATMs and telephones -- 2.3.1 Telecommunication - vitalization through ATM -- 2.4 PCs and online services -- 2.5 E-cash and interactive video -- 2.6 TAM -- 2.7 TRA -- 2.8 Conclusion -- References -- 3 Banking, Fintech, BigTech: emerging challenges for multimedia adoption -- 3.1 Introduction -- 3.2 Trends and patterns of BigTech entry into emerging markets and developing economies (EMDEs) -- 3.2.1 A case study of digital payment trends in India -- 3.3 Drivers of BigTech activity in EMDEs -- 3.4 Pros and cons of BigTech firms entering the financial services 3.4.1 Benefits to the financial services industry from BigTech activities -- 3.4.2 Risks associated with the BigTech firms to enter financial services -- 3.5 Technological growth: opportunities & -- risks for BigTech firms in EMDEs -- 3.5.1 It is the 'DNA' of big tech's business strategy -- 3.5.2 Access to financial services and big data -- 3.5.3 Regulating the financial sector -- 3.5.4 Power in the market and rivalry -- 3.5.5 Coordination of policy and the need for education -- 3.6 Venture capital from EMDEs in facilitating BigTech firms -- 3.6.1 Meaning of venture capital -- 3.6.2 Venture capitalists' impact on BigTech management -- 3.6.3 The BigTech firm and the dependency perspective -- 3.7 Impact of COVID-19 on BigTech firms' activities -- References -- 4 Multimedia technologies in the financial market -- 4.1 Introduction -- 4.2 Cloud-based software-as-a-service (SaaS) -- 4.3 Self-service multimedia banking kiosks -- 4.3.1 SSTs in banking sector: global and local contexts -- 4.4 Image-enabled ATMs -- 4.5 Digital account opening -- 4.5.1 What does digital financial inclusion look like? -- 4.6 Interactive banking portals -- 4.7 Person-to-person (P2P) payments -- 4.7.1 Nonbank-centric P2P payment methods -- 4.7.2 Bank-centric P2P payment methods -- 4.8 Chatbots/virtual personal banker -- 4.8.1 Banking chatbot business -- 4.9 Video banking services -- 4.10 Mobile and TV-based banking -- 4.11 Safe deposit boxes with iris-scanning biometrics -- 4.11.1 Physiological biometrics -- 4.12 Conclusion -- References -- 5 Data analytics in finance -- 5.1 Forecasting economic variables through linear and nonlinear time series analysis -- 5.1.1 Autoregressive dependent framework -- 5.1.2 Models based on moving averages -- 5.1.3 Artificial neural networks in finance -- 5.2 Big data analytics tools for financial forecasting |
| Title | Intelligent Multimedia Technologies for Financial Risk Management |
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